Overview

Dataset statistics

Number of variables47
Number of observations841115
Missing cells1064609
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 GiB
Average record size in memory1.3 KiB

Variable types

Numeric23
Categorical24

Warnings

srch_mobile_app has constant value "0" Constant
srch_date_time has a high cardinality: 23873 distinct values High cardinality
srch_visitor_id has a high cardinality: 23101 distinct values High cardinality
srch_visitor_loc_country has a high cardinality: 151 distinct values High cardinality
srch_visitor_loc_region has a high cardinality: 599 distinct values High cardinality
srch_visitor_loc_city has a high cardinality: 5092 distinct values High cardinality
srch_posa_country has a high cardinality: 67 distinct values High cardinality
srch_ci has a high cardinality: 385 distinct values High cardinality
srch_co has a high cardinality: 376 distinct values High cardinality
srch_currency has a high cardinality: 51 distinct values High cardinality
srch_hcom_destination_id is highly correlated with srch_dest_longitude and 2 other fieldsHigh correlation
srch_dest_longitude is highly correlated with srch_hcom_destination_id and 1 other fieldsHigh correlation
srch_dest_latitude is highly correlated with srch_hcom_destination_id and 1 other fieldsHigh correlation
srch_adults_cnt is highly correlated with srch_rm_cntHigh correlation
srch_rm_cnt is highly correlated with srch_adults_cntHigh correlation
prop_price_without_discount_local is highly correlated with prop_price_with_discount_localHigh correlation
prop_price_without_discount_usd is highly correlated with prop_price_with_discount_usdHigh correlation
prop_price_with_discount_local is highly correlated with prop_price_without_discount_localHigh correlation
prop_price_with_discount_usd is highly correlated with prop_price_without_discount_usdHigh correlation
prop_market_id is highly correlated with srch_hcom_destination_id and 2 other fieldsHigh correlation
prop_room_capacity is highly correlated with prop_review_countHigh correlation
prop_review_count is highly correlated with prop_room_capacityHigh correlation
srch_hcom_destination_id is highly correlated with srch_dest_longitude and 1 other fieldsHigh correlation
srch_dest_longitude is highly correlated with srch_hcom_destination_id and 2 other fieldsHigh correlation
prop_price_without_discount_local is highly correlated with prop_price_without_discount_usd and 2 other fieldsHigh correlation
prop_price_without_discount_usd is highly correlated with prop_price_without_discount_local and 3 other fieldsHigh correlation
prop_price_with_discount_local is highly correlated with prop_price_without_discount_local and 2 other fieldsHigh correlation
prop_price_with_discount_usd is highly correlated with prop_price_without_discount_local and 3 other fieldsHigh correlation
prop_starrating is highly correlated with prop_price_without_discount_usd and 2 other fieldsHigh correlation
prop_market_id is highly correlated with srch_hcom_destination_idHigh correlation
prop_room_capacity is highly correlated with srch_dest_longitude and 1 other fieldsHigh correlation
prop_review_score is highly correlated with prop_starratingHigh correlation
prop_review_count is highly correlated with srch_dest_longitude and 1 other fieldsHigh correlation
srch_hcom_destination_id is highly correlated with prop_market_idHigh correlation
prop_price_without_discount_local is highly correlated with prop_price_without_discount_usd and 1 other fieldsHigh correlation
prop_price_without_discount_usd is highly correlated with prop_price_without_discount_local and 1 other fieldsHigh correlation
prop_price_with_discount_local is highly correlated with prop_price_without_discount_local and 1 other fieldsHigh correlation
prop_price_with_discount_usd is highly correlated with prop_price_without_discount_usd and 1 other fieldsHigh correlation
prop_market_id is highly correlated with srch_hcom_destination_idHigh correlation
prop_room_capacity is highly correlated with prop_review_countHigh correlation
prop_review_count is highly correlated with prop_room_capacityHigh correlation
srch_dest_longitude is highly correlated with prop_super_region and 9 other fieldsHigh correlation
prop_super_region is highly correlated with srch_dest_longitude and 9 other fieldsHigh correlation
prop_submarket_id is highly correlated with srch_dest_longitude and 8 other fieldsHigh correlation
srch_dest_latitude is highly correlated with srch_dest_longitude and 9 other fieldsHigh correlation
srch_device is highly correlated with srch_mobile_boolHigh correlation
srch_posa_continent is highly correlated with srch_dest_longitude and 7 other fieldsHigh correlation
prop_room_capacity is highly correlated with prop_review_countHigh correlation
prop_key is highly correlated with prop_market_idHigh correlation
prop_price_with_discount_local is highly correlated with srch_posa_country and 2 other fieldsHigh correlation
srch_co_day is highly correlated with srch_ci_dayHigh correlation
prop_review_score is highly correlated with prop_starratingHigh correlation
prop_starrating is highly correlated with prop_review_scoreHigh correlation
prop_country is highly correlated with srch_dest_longitude and 9 other fieldsHigh correlation
prop_price_without_discount_usd is highly correlated with prop_price_with_discount_usdHigh correlation
srch_posa_country is highly correlated with srch_dest_longitude and 10 other fieldsHigh correlation
srch_hcom_destination_id is highly correlated with srch_dest_longitude and 9 other fieldsHigh correlation
prop_review_count is highly correlated with prop_room_capacityHigh correlation
srch_rm_cnt is highly correlated with srch_adults_cntHigh correlation
srch_adults_cnt is highly correlated with srch_rm_cntHigh correlation
srch_ci_day is highly correlated with srch_co_dayHigh correlation
prop_price_without_discount_local is highly correlated with prop_price_with_discount_local and 1 other fieldsHigh correlation
srch_currency is highly correlated with srch_dest_longitude and 11 other fieldsHigh correlation
prop_continent is highly correlated with srch_dest_longitude and 9 other fieldsHigh correlation
prop_market_id is highly correlated with srch_dest_longitude and 9 other fieldsHigh correlation
srch_mobile_bool is highly correlated with srch_deviceHigh correlation
prop_price_with_discount_usd is highly correlated with prop_price_without_discount_usdHigh correlation
srch_device is highly correlated with srch_mobile_app and 1 other fieldsHigh correlation
prop_hostel_bool is highly correlated with srch_mobile_appHigh correlation
srch_posa_continent is highly correlated with srch_mobile_app and 2 other fieldsHigh correlation
srch_local_date is highly correlated with srch_mobile_appHigh correlation
prop_continent is highly correlated with srch_mobile_app and 4 other fieldsHigh correlation
srch_visitor_wr_member is highly correlated with srch_mobile_appHigh correlation
srch_mobile_app is highly correlated with srch_device and 15 other fieldsHigh correlation
prop_imp_drr is highly correlated with srch_mobile_appHigh correlation
prop_super_region is highly correlated with prop_continent and 4 other fieldsHigh correlation
prop_brand_bool is highly correlated with srch_mobile_appHigh correlation
prop_booking_bool is highly correlated with srch_mobile_appHigh correlation
prop_dotd_bool is highly correlated with srch_mobile_appHigh correlation
prop_travelad_bool is highly correlated with srch_mobile_appHigh correlation
srch_posa_country is highly correlated with srch_posa_continent and 4 other fieldsHigh correlation
prop_country is highly correlated with prop_continent and 2 other fieldsHigh correlation
srch_mobile_bool is highly correlated with srch_device and 1 other fieldsHigh correlation
srch_currency is highly correlated with srch_posa_continent and 4 other fieldsHigh correlation
srch_visitor_wr_member has 444878 (52.9%) missing values Missing
srch_posa_continent has 485248 (57.7%) missing values Missing
srch_currency has 134104 (15.9%) missing values Missing
prop_price_without_discount_local is highly skewed (γ1 = 63.25387336) Skewed
prop_price_without_discount_usd is highly skewed (γ1 = 110.1131851) Skewed
prop_price_with_discount_local is highly skewed (γ1 = 51.50835913) Skewed
prop_price_with_discount_usd is highly skewed (γ1 = 92.09231784) Skewed
srch_bw has 68898 (8.2%) zeros Zeros
srch_children_cnt has 760192 (90.4%) zeros Zeros
prop_starrating has 10593 (1.3%) zeros Zeros

Reproduction

Analysis started2021-07-06 22:53:05.366356
Analysis finished2021-07-06 22:58:39.744931
Duration5 minutes and 34.38 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

srch_id
Real number (ℝ)

Distinct24013
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17242327.95
Minimum-2147275208
Maximum2147265531
Zeros0
Zeros (%)0.0%
Negative413244
Negative (%)49.1%
Memory size6.4 MiB
2021-07-06T23:58:39.828543image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-2147275208
5-th percentile-1929852848
Q1-1034005034
median31939516
Q31081645807
95-th percentile1926418499
Maximum2147265531
Range4294540739
Interquartile range (IQR)2115650841

Descriptive statistics

Standard deviation1231757238
Coefficient of variation (CV)71.43798922
Kurtosis-1.184776024
Mean17242327.95
Median Absolute Deviation (MAD)1057033603
Skewness-0.02450013705
Sum1.450278067 × 1013
Variance1.517225894 × 1018
MonotonicityNot monotonic
2021-07-06T23:58:39.919440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60981410054
 
< 0.1%
2456574454
 
< 0.1%
90210461554
 
< 0.1%
184500911954
 
< 0.1%
-186524910154
 
< 0.1%
-5795310654
 
< 0.1%
-114101649154
 
< 0.1%
85172824854
 
< 0.1%
89385967554
 
< 0.1%
-88281610454
 
< 0.1%
Other values (24003)840575
99.9%
ValueCountFrequency (%)
-214727520810
 
< 0.1%
-214725333410
 
< 0.1%
-214649928250
< 0.1%
-214632667451
< 0.1%
-214629839153
< 0.1%
-214622662910
 
< 0.1%
-21461310158
 
< 0.1%
-214582944452
< 0.1%
-214578755151
< 0.1%
-214548945910
 
< 0.1%
ValueCountFrequency (%)
214726553110
 
< 0.1%
214696247410
 
< 0.1%
214679024649
< 0.1%
214673064410
 
< 0.1%
214660918052
< 0.1%
214640518244
< 0.1%
214636097310
 
< 0.1%
214617524010
 
< 0.1%
214604145350
< 0.1%
214597138210
 
< 0.1%

prop_key
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6330
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean818556.8818
Minimum240746
Maximum3949856
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:40.014499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum240746
5-th percentile244333
Q1255952
median277742
Q3478133
95-th percentile3799382
Maximum3949856
Range3709110
Interquartile range (IQR)222181

Descriptive statistics

Standard deviation1141255.418
Coefficient of variation (CV)1.394228604
Kurtosis1.71624615
Mean818556.8818
Median Absolute Deviation (MAD)29007
Skewness1.871536006
Sum6.885004716 × 1011
Variance1.30246393 × 1012
MonotonicityNot monotonic
2021-07-06T23:58:40.106723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2590464862
 
0.6%
2417704641
 
0.6%
2633194615
 
0.5%
2634844580
 
0.5%
2525834514
 
0.5%
2732684465
 
0.5%
2451984354
 
0.5%
5529654347
 
0.5%
2647524324
 
0.5%
5529664317
 
0.5%
Other values (6320)796096
94.6%
ValueCountFrequency (%)
2407461063
 
0.1%
2407543015
0.4%
240790342
 
< 0.1%
240800935
 
0.1%
2408041
 
< 0.1%
2408301
 
< 0.1%
24083490
 
< 0.1%
240886112
 
< 0.1%
240898381
 
< 0.1%
24091996
 
< 0.1%
ValueCountFrequency (%)
39498564
< 0.1%
39497201
 
< 0.1%
39495511
 
< 0.1%
39494182
< 0.1%
39493111
 
< 0.1%
39492451
 
< 0.1%
39491341
 
< 0.1%
39490831
 
< 0.1%
39490451
 
< 0.1%
39488844
< 0.1%

srch_date_time
Categorical

HIGH CARDINALITY

Distinct23873
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size61.0 MiB
2014-09-22 23:33:00
 
106
2014-09-15 20:19:04
 
105
2014-09-09 18:00:56
 
105
2014-09-17 16:24:07
 
105
2014-09-26 19:52:29
 
105
Other values (23868)
840589 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters15981185
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)< 0.1%

Sample

1st row2014-09-13 18:37:32
2nd row2014-09-13 18:37:32
3rd row2014-09-13 18:37:32
4th row2014-09-13 18:37:32
5th row2014-09-13 18:37:32

Common Values

ValueCountFrequency (%)
2014-09-22 23:33:00106
 
< 0.1%
2014-09-15 20:19:04105
 
< 0.1%
2014-09-09 18:00:56105
 
< 0.1%
2014-09-17 16:24:07105
 
< 0.1%
2014-09-26 19:52:29105
 
< 0.1%
2014-09-02 18:38:20105
 
< 0.1%
2014-09-18 21:51:58104
 
< 0.1%
2014-09-02 17:36:25103
 
< 0.1%
2014-09-15 16:25:36103
 
< 0.1%
2014-09-14 21:31:53103
 
< 0.1%
Other values (23863)840071
99.9%

Length

2021-07-06T23:58:40.301062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2014-09-0339487
 
2.3%
2014-09-0938261
 
2.3%
2014-09-0837843
 
2.2%
2014-09-0237839
 
2.2%
2014-09-0437662
 
2.2%
2014-09-1737515
 
2.2%
2014-09-1036488
 
2.2%
2014-09-1535691
 
2.1%
2014-09-1135239
 
2.1%
2014-09-1234176
 
2.0%
Other values (20596)1312029
78.0%

Most occurring characters

ValueCountFrequency (%)
02714716
17.0%
12271423
14.2%
21907976
11.9%
-1682230
10.5%
:1682230
10.5%
41449681
9.1%
91127189
7.1%
841115
 
5.3%
3645781
 
4.0%
5613574
 
3.8%
Other values (3)1045270
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11775610
73.7%
Dash Punctuation1682230
 
10.5%
Other Punctuation1682230
 
10.5%
Space Separator841115
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02714716
23.1%
12271423
19.3%
21907976
16.2%
41449681
12.3%
91127189
9.6%
3645781
 
5.5%
5613574
 
5.2%
6352576
 
3.0%
7347590
 
3.0%
8345104
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
-1682230
100.0%
Space Separator
ValueCountFrequency (%)
841115
100.0%
Other Punctuation
ValueCountFrequency (%)
:1682230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15981185
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02714716
17.0%
12271423
14.2%
21907976
11.9%
-1682230
10.5%
:1682230
10.5%
41449681
9.1%
91127189
7.1%
841115
 
5.3%
3645781
 
4.0%
5613574
 
3.8%
Other values (3)1045270
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII15981185
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02714716
17.0%
12271423
14.2%
21907976
11.9%
-1682230
10.5%
:1682230
10.5%
41449681
9.1%
91127189
7.1%
841115
 
5.3%
3645781
 
4.0%
5613574
 
3.8%
Other values (3)1045270
 
6.5%

srch_visitor_id
Categorical

HIGH CARDINALITY

Distinct23101
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size74.6 MiB
0fac9b35-d7b7-4b71-b275-7da40310ea2e
 
800
cb732dfb-f598-4e2d-a9cd-c915bd12e36f
 
681
5e866128-7338-4be3-a7cd-156e510bab06
 
503
2513167f-f397-492a-86aa-94ec58496028
 
320
c1838c1f-f797-41c2-82e1-b366d3bb2b76
 
308
Other values (23096)
838503 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters30280140
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)< 0.1%

Sample

1st row9373b009-4e10-495a-afae-204dd1fe4b7c
2nd row9373b009-4e10-495a-afae-204dd1fe4b7c
3rd row9373b009-4e10-495a-afae-204dd1fe4b7c
4th row9373b009-4e10-495a-afae-204dd1fe4b7c
5th row9373b009-4e10-495a-afae-204dd1fe4b7c

Common Values

ValueCountFrequency (%)
0fac9b35-d7b7-4b71-b275-7da40310ea2e800
 
0.1%
cb732dfb-f598-4e2d-a9cd-c915bd12e36f681
 
0.1%
5e866128-7338-4be3-a7cd-156e510bab06503
 
0.1%
2513167f-f397-492a-86aa-94ec58496028320
 
< 0.1%
c1838c1f-f797-41c2-82e1-b366d3bb2b76308
 
< 0.1%
8d0876e7-9070-4ffe-825f-51cd225c68eb285
 
< 0.1%
fa32fabe-9de9-4136-a615-b630792bb9db271
 
< 0.1%
f8acb1f2-b629-4a0e-8091-f7f8b01d6c9a261
 
< 0.1%
76638035-90fd-48ac-8c3a-b27c7ee5f705259
 
< 0.1%
e48f2ac3-b53d-45d8-a35e-10aec811af6b224
 
< 0.1%
Other values (23091)837203
99.5%

Length

2021-07-06T23:58:40.481715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0fac9b35-d7b7-4b71-b275-7da40310ea2e800
 
0.1%
cb732dfb-f598-4e2d-a9cd-c915bd12e36f681
 
0.1%
5e866128-7338-4be3-a7cd-156e510bab06503
 
0.1%
2513167f-f397-492a-86aa-94ec58496028320
 
< 0.1%
c1838c1f-f797-41c2-82e1-b366d3bb2b76308
 
< 0.1%
8d0876e7-9070-4ffe-825f-51cd225c68eb285
 
< 0.1%
fa32fabe-9de9-4136-a615-b630792bb9db271
 
< 0.1%
f8acb1f2-b629-4a0e-8091-f7f8b01d6c9a261
 
< 0.1%
76638035-90fd-48ac-8c3a-b27c7ee5f705259
 
< 0.1%
e48f2ac3-b53d-45d8-a35e-10aec811af6b224
 
< 0.1%
Other values (23091)837203
99.5%

Most occurring characters

ValueCountFrequency (%)
-3364460
 
11.1%
42403151
 
7.9%
91792973
 
5.9%
81789376
 
5.9%
b1786075
 
5.9%
a1773699
 
5.9%
61590630
 
5.3%
01587671
 
5.2%
c1585333
 
5.2%
71583028
 
5.2%
Other values (7)11023744
36.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17051389
56.3%
Lowercase Letter9864291
32.6%
Dash Punctuation3364460
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
42403151
14.1%
91792973
10.5%
81789376
10.5%
61590630
9.3%
01587671
9.3%
71583028
9.3%
31579839
9.3%
21579084
9.3%
51574243
9.2%
11571394
9.2%
Lowercase Letter
ValueCountFrequency (%)
b1786075
18.1%
a1773699
18.0%
c1585333
16.1%
f1582974
16.0%
e1570692
15.9%
d1565518
15.9%
Dash Punctuation
ValueCountFrequency (%)
-3364460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common20415849
67.4%
Latin9864291
32.6%

Most frequent character per script

Common
ValueCountFrequency (%)
-3364460
16.5%
42403151
11.8%
91792973
8.8%
81789376
8.8%
61590630
7.8%
01587671
7.8%
71583028
7.8%
31579839
7.7%
21579084
7.7%
51574243
7.7%
Latin
ValueCountFrequency (%)
b1786075
18.1%
a1773699
18.0%
c1585333
16.1%
f1582974
16.0%
e1570692
15.9%
d1565518
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII30280140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-3364460
 
11.1%
42403151
 
7.9%
91792973
 
5.9%
81789376
 
5.9%
b1786075
 
5.9%
a1773699
 
5.9%
61590630
 
5.3%
01587671
 
5.2%
c1585333
 
5.2%
71583028
 
5.2%
Other values (7)11023744
36.4%

srch_visitor_visit_nbr
Real number (ℝ≥0)

Distinct267
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.32597683
Minimum1
Maximum1082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:40.566315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q310
95-th percentile51
Maximum1082
Range1081
Interquartile range (IQR)9

Descriptive statistics

Standard deviation31.78893561
Coefficient of variation (CV)2.579019582
Kurtosis192.6232622
Mean12.32597683
Median Absolute Deviation (MAD)2
Skewness10.43091904
Sum10367564
Variance1010.536427
MonotonicityNot monotonic
2021-07-06T23:58:40.657591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1240336
28.6%
2111575
13.3%
373442
 
8.7%
451948
 
6.2%
541687
 
5.0%
630522
 
3.6%
724148
 
2.9%
823426
 
2.8%
918830
 
2.2%
1115188
 
1.8%
Other values (257)210013
25.0%
ValueCountFrequency (%)
1240336
28.6%
2111575
13.3%
373442
 
8.7%
451948
 
6.2%
541687
 
5.0%
630522
 
3.6%
724148
 
2.9%
823426
 
2.8%
918830
 
2.2%
1014942
 
1.8%
ValueCountFrequency (%)
108252
< 0.1%
83250
< 0.1%
72510
 
< 0.1%
72050
< 0.1%
60349
< 0.1%
59349
< 0.1%
58650
< 0.1%
56849
< 0.1%
51852
< 0.1%
49752
< 0.1%

srch_visitor_loc_country
Categorical

HIGH CARDINALITY

Distinct151
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.2 MiB
UNITED STATES OF AMERICA
463183 
UNITED KINGDOM
51295 
SWEDEN
 
31177
FRANCE
 
24430
JAPAN
 
23331
Other values (146)
247699 

Length

Max length24
Median length24
Mean length16.85426725
Min length3

Characters and Unicode

Total characters14176377
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTWN
2nd rowTWN
3rd rowTWN
4th rowTWN
5th rowTWN

Common Values

ValueCountFrequency (%)
UNITED STATES OF AMERICA463183
55.1%
UNITED KINGDOM51295
 
6.1%
SWEDEN31177
 
3.7%
FRANCE24430
 
2.9%
JAPAN23331
 
2.8%
NORWAY22803
 
2.7%
SOUTH KOREA21973
 
2.6%
CANADA21272
 
2.5%
HONG KONG17774
 
2.1%
BRAZIL15633
 
1.9%
Other values (141)148244
 
17.6%

Length

2021-07-06T23:58:40.840895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united515730
22.0%
of463426
19.7%
states463183
19.7%
america463183
19.7%
kingdom51295
 
2.2%
sweden31177
 
1.3%
france24430
 
1.0%
japan23331
 
1.0%
south23100
 
1.0%
norway22803
 
1.0%
Other values (168)265057
11.3%

Most occurring characters

ValueCountFrequency (%)
A1752399
12.4%
E1642867
11.6%
T1515444
10.7%
1505600
10.6%
I1158174
8.2%
S1036417
 
7.3%
N843327
 
5.9%
D668867
 
4.7%
O651174
 
4.6%
R633986
 
4.5%
Other values (19)2768122
19.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter12665667
89.3%
Space Separator1505600
 
10.6%
Other Punctuation5110
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1752399
13.8%
E1642867
13.0%
T1515444
12.0%
I1158174
9.1%
S1036417
8.2%
N843327
 
6.7%
D668867
 
5.3%
O651174
 
5.1%
R633986
 
5.0%
U564878
 
4.5%
Other values (16)2198134
17.4%
Other Punctuation
ValueCountFrequency (%)
&4547
89.0%
'563
 
11.0%
Space Separator
ValueCountFrequency (%)
1505600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12665667
89.3%
Common1510710
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1752399
13.8%
E1642867
13.0%
T1515444
12.0%
I1158174
9.1%
S1036417
8.2%
N843327
 
6.7%
D668867
 
5.3%
O651174
 
5.1%
R633986
 
5.0%
U564878
 
4.5%
Other values (16)2198134
17.4%
Common
ValueCountFrequency (%)
1505600
99.7%
&4547
 
0.3%
'563
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII14176377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A1752399
12.4%
E1642867
11.6%
T1515444
10.7%
1505600
10.6%
I1158174
8.2%
S1036417
 
7.3%
N843327
 
5.9%
D668867
 
4.7%
O651174
 
4.6%
R633986
 
4.5%
Other values (19)2768122
19.5%

srch_visitor_loc_region
Categorical

HIGH CARDINALITY

Distinct599
Distinct (%)0.1%
Missing123
Missing (%)< 0.1%
Memory size47.5 MiB
CA
115310 
NY
 
45051
FL
 
34241
IL
 
29272
11
 
24518
Other values (594)
592600 

Length

Max length9
Median length2
Mean length2.217154265
Min length1

Characters and Unicode

Total characters1864609
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTPE
2nd rowTPE
3rd rowTPE
4th rowTPE
5th rowTPE

Common Values

ValueCountFrequency (%)
CA115310
 
13.7%
NY45051
 
5.4%
FL34241
 
4.1%
IL29272
 
3.5%
1124518
 
2.9%
TX22965
 
2.7%
NO REGION21151
 
2.5%
AB17923
 
2.1%
MA17496
 
2.1%
AZ16596
 
2.0%
Other values (589)496469
59.0%

Length

2021-07-06T23:58:41.025381image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca115310
 
13.4%
ny45051
 
5.2%
fl34241
 
4.0%
il29272
 
3.4%
1124518
 
2.8%
tx22965
 
2.7%
region21151
 
2.5%
no21151
 
2.5%
ab17923
 
2.1%
ma17496
 
2.0%
Other values (590)513065
59.5%

Most occurring characters

ValueCountFrequency (%)
A241435
 
12.9%
N175429
 
9.4%
C166421
 
8.9%
197987
 
5.3%
O92296
 
4.9%
I91884
 
4.9%
L91790
 
4.9%
M73355
 
3.9%
T64037
 
3.4%
Y53386
 
2.9%
Other values (28)716589
38.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1607962
86.2%
Decimal Number235315
 
12.6%
Space Separator21151
 
1.1%
Other Punctuation181
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A241435
15.0%
N175429
 
10.9%
C166421
 
10.3%
O92296
 
5.7%
I91884
 
5.7%
L91790
 
5.7%
M73355
 
4.6%
T64037
 
4.0%
Y53386
 
3.3%
R47126
 
2.9%
Other values (16)510803
31.8%
Decimal Number
ValueCountFrequency (%)
197987
41.6%
031648
 
13.4%
330802
 
13.1%
819199
 
8.2%
218305
 
7.8%
417625
 
7.5%
76495
 
2.8%
66272
 
2.7%
54146
 
1.8%
92836
 
1.2%
Space Separator
ValueCountFrequency (%)
21151
100.0%
Other Punctuation
ValueCountFrequency (%)
?181
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1607962
86.2%
Common256647
 
13.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A241435
15.0%
N175429
 
10.9%
C166421
 
10.3%
O92296
 
5.7%
I91884
 
5.7%
L91790
 
5.7%
M73355
 
4.6%
T64037
 
4.0%
Y53386
 
3.3%
R47126
 
2.9%
Other values (16)510803
31.8%
Common
ValueCountFrequency (%)
197987
38.2%
031648
 
12.3%
330802
 
12.0%
21151
 
8.2%
819199
 
7.5%
218305
 
7.1%
417625
 
6.9%
76495
 
2.5%
66272
 
2.4%
54146
 
1.6%
Other values (2)3017
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1864609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A241435
 
12.9%
N175429
 
9.4%
C166421
 
8.9%
197987
 
5.3%
O92296
 
4.9%
I91884
 
4.9%
L91790
 
4.9%
M73355
 
3.9%
T64037
 
3.4%
Y53386
 
2.9%
Other values (28)716589
38.4%

srch_visitor_loc_city
Categorical

HIGH CARDINALITY

Distinct5092
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size52.5 MiB
NEW YORK
 
21172
SEOUL
 
16711
LOS ANGELES
 
14358
HONG KONG
 
13451
LAS VEGAS
 
10850
Other values (5087)
764573 

Length

Max length26
Median length8
Mean length8.473692658
Min length1

Characters and Unicode

Total characters7127350
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowTAIPEI
2nd rowTAIPEI
3rd rowTAIPEI
4th rowTAIPEI
5th rowTAIPEI

Common Values

ValueCountFrequency (%)
NEW YORK21172
 
2.5%
SEOUL16711
 
2.0%
LOS ANGELES14358
 
1.7%
HONG KONG13451
 
1.6%
LAS VEGAS10850
 
1.3%
CHICAGO10661
 
1.3%
STOCKHOLM8497
 
1.0%
TOKYO8285
 
1.0%
WASHINGTON7095
 
0.8%
SAN FRANCISCO6746
 
0.8%
Other values (5082)723289
86.0%

Length

2021-07-06T23:58:41.217770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new23820
 
2.2%
san22546
 
2.1%
york22196
 
2.0%
city19071
 
1.8%
seoul16711
 
1.5%
los15207
 
1.4%
angeles14460
 
1.3%
hong13451
 
1.2%
kong13451
 
1.2%
las11541
 
1.1%
Other values (5006)916536
84.2%

Most occurring characters

ValueCountFrequency (%)
A749848
 
10.5%
O638370
 
9.0%
E617265
 
8.7%
N585903
 
8.2%
L467029
 
6.6%
S448118
 
6.3%
R429330
 
6.0%
I425839
 
6.0%
T354416
 
5.0%
247875
 
3.5%
Other values (21)2163357
30.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6866306
96.3%
Space Separator247875
 
3.5%
Dash Punctuation12395
 
0.2%
Other Punctuation774
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A749848
 
10.9%
O638370
 
9.3%
E617265
 
9.0%
N585903
 
8.5%
L467029
 
6.8%
S448118
 
6.5%
R429330
 
6.3%
I425839
 
6.2%
T354416
 
5.2%
H234899
 
3.4%
Other values (16)1915289
27.9%
Other Punctuation
ValueCountFrequency (%)
'416
53.7%
?234
30.2%
.124
 
16.0%
Space Separator
ValueCountFrequency (%)
247875
100.0%
Dash Punctuation
ValueCountFrequency (%)
-12395
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6866306
96.3%
Common261044
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A749848
 
10.9%
O638370
 
9.3%
E617265
 
9.0%
N585903
 
8.5%
L467029
 
6.8%
S448118
 
6.5%
R429330
 
6.3%
I425839
 
6.2%
T354416
 
5.2%
H234899
 
3.4%
Other values (16)1915289
27.9%
Common
ValueCountFrequency (%)
247875
95.0%
-12395
 
4.7%
'416
 
0.2%
?234
 
0.1%
.124
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII7127350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A749848
 
10.5%
O638370
 
9.0%
E617265
 
8.7%
N585903
 
8.2%
L467029
 
6.6%
S448118
 
6.3%
R429330
 
6.0%
I425839
 
6.0%
T354416
 
5.0%
247875
 
3.5%
Other values (21)2163357
30.4%

srch_visitor_wr_member
Categorical

HIGH CORRELATION
MISSING

Distinct9
Distinct (%)< 0.1%
Missing444878
Missing (%)52.9%
Memory size50.9 MiB
Not Signed In|Returning Visitor|Not FC Member
186401 
Not Signed In|New Visitor|Not FC Member
90491 
Signed In|WR Member|Not FC Member
64361 
Signed in - Persistent|WR Member|Not FC Member
49429 
Signed in - Persistent|WR Member|Remembered FC Member
 
3411
Other values (4)
 
2144

Length

Max length57
Median length45
Mean length41.82906442
Min length29

Characters and Unicode

Total characters16574223
Distinct characters26
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSigned in - Persistent|WR Member|Remembered FC Member
2nd rowSigned in - Persistent|WR Member|Remembered FC Member
3rd rowSigned in - Persistent|WR Member|Remembered FC Member
4th rowSigned in - Persistent|WR Member|Remembered FC Member
5th rowSigned in - Persistent|WR Member|Remembered FC Member

Common Values

ValueCountFrequency (%)
Not Signed In|Returning Visitor|Not FC Member186401
22.2%
Not Signed In|New Visitor|Not FC Member90491
 
10.8%
Signed In|WR Member|Not FC Member64361
 
7.7%
Signed in - Persistent|WR Member|Not FC Member49429
 
5.9%
Signed in - Persistent|WR Member|Remembered FC Member3411
 
0.4%
Signed In|WR Member|FC Member1077
 
0.1%
Signed In|Not WR Member|Not FC Member540
 
0.1%
Signed in - Persistent|Not WR Member|Not FC Member379
 
< 0.1%
Signed in - Persistent|Not WR Member|Remembered FC Member148
 
< 0.1%
(Missing)444878
52.9%

Length

2021-07-06T23:58:41.368831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-06T23:58:41.421923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
signed396237
16.8%
member396237
16.8%
fc395160
16.7%
visitor|not276892
11.7%
not276892
11.7%
in|returning186401
7.9%
member|not114709
 
4.9%
in|new90491
 
3.8%
in|wr65438
 
2.8%
53367
 
2.3%
Other values (7)112977
 
4.8%

Most occurring characters

ValueCountFrequency (%)
1968564
 
11.9%
e1825263
 
11.0%
i1243156
 
7.5%
t1239587
 
7.5%
n1218643
 
7.4%
r1035801
 
6.2%
o946452
 
5.7%
|792474
 
4.8%
N760051
 
4.6%
g582638
 
3.5%
Other values (16)4961594
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10193695
61.5%
Uppercase Letter3566123
 
21.5%
Space Separator1968564
 
11.9%
Math Symbol792474
 
4.8%
Dash Punctuation53367
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1825263
17.9%
i1243156
12.2%
t1239587
12.2%
n1218643
12.0%
r1035801
10.2%
o946452
9.3%
g582638
 
5.7%
m522700
 
5.1%
b519141
 
5.1%
d399796
 
3.9%
Other values (3)660518
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
N760051
21.3%
M515582
14.5%
S396237
11.1%
F396237
11.1%
C396237
11.1%
I342870
9.6%
R309305
8.7%
V276892
 
7.8%
W119345
 
3.3%
P53367
 
1.5%
Space Separator
ValueCountFrequency (%)
1968564
100.0%
Dash Punctuation
ValueCountFrequency (%)
-53367
100.0%
Math Symbol
ValueCountFrequency (%)
|792474
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13759818
83.0%
Common2814405
 
17.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1825263
13.3%
i1243156
 
9.0%
t1239587
 
9.0%
n1218643
 
8.9%
r1035801
 
7.5%
o946452
 
6.9%
N760051
 
5.5%
g582638
 
4.2%
m522700
 
3.8%
b519141
 
3.8%
Other values (13)3866386
28.1%
Common
ValueCountFrequency (%)
1968564
69.9%
|792474
28.2%
-53367
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII16574223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1968564
 
11.9%
e1825263
 
11.0%
i1243156
 
7.5%
t1239587
 
7.5%
n1218643
 
7.4%
r1035801
 
6.2%
o946452
 
5.7%
|792474
 
4.8%
N760051
 
4.6%
g582638
 
3.5%
Other values (16)4961594
29.9%

srch_posa_continent
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing485248
Missing (%)57.7%
Memory size36.0 MiB
EUROPE
207181 
ASIA
110575 
LATAM
32440 
OCEANIA
 
5671

Length

Max length7
Median length6
Mean length5.303338045
Min length4

Characters and Unicode

Total characters1887283
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowASIA
2nd rowASIA
3rd rowASIA
4th rowASIA
5th rowASIA

Common Values

ValueCountFrequency (%)
EUROPE207181
24.6%
ASIA110575
 
13.1%
LATAM32440
 
3.9%
OCEANIA5671
 
0.7%
(Missing)485248
57.7%

Length

2021-07-06T23:58:41.606575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-06T23:58:41.657790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
europe207181
58.2%
asia110575
31.1%
latam32440
 
9.1%
oceania5671
 
1.6%

Most occurring characters

ValueCountFrequency (%)
E420033
22.3%
A297372
15.8%
O212852
11.3%
U207181
11.0%
R207181
11.0%
P207181
11.0%
I116246
 
6.2%
S110575
 
5.9%
L32440
 
1.7%
T32440
 
1.7%
Other values (3)43782
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1887283
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E420033
22.3%
A297372
15.8%
O212852
11.3%
U207181
11.0%
R207181
11.0%
P207181
11.0%
I116246
 
6.2%
S110575
 
5.9%
L32440
 
1.7%
T32440
 
1.7%
Other values (3)43782
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Latin1887283
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E420033
22.3%
A297372
15.8%
O212852
11.3%
U207181
11.0%
R207181
11.0%
P207181
11.0%
I116246
 
6.2%
S110575
 
5.9%
L32440
 
1.7%
T32440
 
1.7%
Other values (3)43782
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1887283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E420033
22.3%
A297372
15.8%
O212852
11.3%
U207181
11.0%
R207181
11.0%
P207181
11.0%
I116246
 
6.2%
S110575
 
5.9%
L32440
 
1.7%
T32440
 
1.7%
Other values (3)43782
 
2.3%

srch_posa_country
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.7 MiB
US
463532 
UNITED KINGDOM
51033 
SWEDEN
 
32530
FRANCE
 
29798
JAPAN
 
23125
Other values (62)
241097 

Length

Max length25
Median length2
Mean length4.992647854
Min length2

Characters and Unicode

Total characters4199391
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTAIWAN, REPUBLIC OF CHINA
2nd rowTAIWAN, REPUBLIC OF CHINA
3rd rowTAIWAN, REPUBLIC OF CHINA
4th rowTAIWAN, REPUBLIC OF CHINA
5th rowTAIWAN, REPUBLIC OF CHINA

Common Values

ValueCountFrequency (%)
US463532
55.1%
UNITED KINGDOM51033
 
6.1%
SWEDEN32530
 
3.9%
FRANCE29798
 
3.5%
JAPAN23125
 
2.7%
SOUTH KOREA22901
 
2.7%
NORWAY22734
 
2.7%
CANADA21716
 
2.6%
EMEA18278
 
2.2%
HONG KONG16671
 
2.0%
Other values (57)138797
 
16.5%

Length

2021-07-06T23:58:41.813083image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us463532
46.5%
kingdom51033
 
5.1%
united51033
 
5.1%
sweden32530
 
3.3%
france29798
 
3.0%
south24022
 
2.4%
japan23125
 
2.3%
korea22901
 
2.3%
china22854
 
2.3%
norway22734
 
2.3%
Other values (68)253106
25.4%

Most occurring characters

ValueCountFrequency (%)
S572527
13.6%
U570778
13.6%
A402728
 
9.6%
N381830
 
9.1%
E289807
 
6.9%
I252941
 
6.0%
D198646
 
4.7%
O191188
 
4.6%
R168528
 
4.0%
155553
 
3.7%
Other values (18)1014865
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4023471
95.8%
Space Separator155553
 
3.7%
Other Punctuation20367
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S572527
14.2%
U570778
14.2%
A402728
10.0%
N381830
9.5%
E289807
 
7.2%
I252941
 
6.3%
D198646
 
4.9%
O191188
 
4.8%
R168528
 
4.2%
T113918
 
2.8%
Other values (15)880580
21.9%
Other Punctuation
ValueCountFrequency (%)
,16430
80.7%
&3937
 
19.3%
Space Separator
ValueCountFrequency (%)
155553
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4023471
95.8%
Common175920
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
S572527
14.2%
U570778
14.2%
A402728
10.0%
N381830
9.5%
E289807
 
7.2%
I252941
 
6.3%
D198646
 
4.9%
O191188
 
4.8%
R168528
 
4.2%
T113918
 
2.8%
Other values (15)880580
21.9%
Common
ValueCountFrequency (%)
155553
88.4%
,16430
 
9.3%
&3937
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4199391
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S572527
13.6%
U570778
13.6%
A402728
 
9.6%
N381830
 
9.1%
E289807
 
6.9%
I252941
 
6.0%
D198646
 
4.7%
O191188
 
4.6%
R168528
 
4.0%
155553
 
3.7%
Other values (18)1014865
24.2%

srch_hcom_destination_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1194308.584
Minimum504261
Maximum1506246
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:41.876799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum504261
5-th percentile504261
Q1726784
median1497539
Q31504033
95-th percentile1506246
Maximum1506246
Range1001985
Interquartile range (IQR)777249

Descriptive statistics

Standard deviation423726.7211
Coefficient of variation (CV)0.3547883075
Kurtosis-1.397723401
Mean1194308.584
Median Absolute Deviation (MAD)8707
Skewness-0.7206645034
Sum1.004550865 × 1012
Variance1.795443342 × 1011
MonotonicityNot monotonic
2021-07-06T23:58:41.938058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1504033229512
27.3%
1506246188330
22.4%
549499108661
12.9%
149753992281
11.0%
50426165033
 
7.7%
140471144181
 
5.3%
72678439407
 
4.7%
72866031007
 
3.7%
75981826789
 
3.2%
71249115914
 
1.9%
ValueCountFrequency (%)
50426165033
 
7.7%
549499108661
12.9%
71249115914
 
1.9%
72678439407
 
4.7%
72866031007
 
3.7%
75981826789
 
3.2%
140471144181
 
5.3%
149753992281
11.0%
1504033229512
27.3%
1506246188330
22.4%
ValueCountFrequency (%)
1506246188330
22.4%
1504033229512
27.3%
149753992281
11.0%
140471144181
 
5.3%
75981826789
 
3.2%
72866031007
 
3.7%
72678439407
 
4.7%
71249115914
 
1.9%
549499108661
12.9%
50426165033
 
7.7%

srch_dest_longitude
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-45.89280311
Minimum-115.1728745
Maximum139.7599945
Zeros0
Zeros (%)0.0%
Negative662965
Negative (%)78.8%
Memory size6.4 MiB
2021-07-06T23:58:41.997651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-115.1728745
5-th percentile-115.1728745
Q1-115.1728745
median-73.98647308
Q3-0.127803996
95-th percentile135.4981537
Maximum139.7599945
Range254.932869
Interquartile range (IQR)115.0450705

Descriptive statistics

Standard deviation77.70605067
Coefficient of variation (CV)-1.693207767
Kurtosis0.6220493695
Mean-45.89280311
Median Absolute Deviation (MAD)41.18640142
Skewness1.285501783
Sum-38601125.08
Variance6038.23031
MonotonicityNot monotonic
2021-07-06T23:58:42.062535image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-115.1728745229512
27.3%
-73.98647308188330
22.4%
-0.127803996108661
12.9%
-87.6281280592281
11.0%
2.3437565033
 
7.7%
-81.3790435844181
 
5.3%
139.759994539407
 
4.7%
135.498153731007
 
3.7%
126.982574526789
 
3.2%
12.500920315914
 
1.9%
ValueCountFrequency (%)
-115.1728745229512
27.3%
-87.6281280592281
11.0%
-81.3790435844181
 
5.3%
-73.98647308188330
22.4%
-0.127803996108661
12.9%
2.3437565033
 
7.7%
12.500920315914
 
1.9%
126.982574526789
 
3.2%
135.498153731007
 
3.7%
139.759994539407
 
4.7%
ValueCountFrequency (%)
139.759994539407
 
4.7%
135.498153731007
 
3.7%
126.982574526789
 
3.2%
12.500920315914
 
1.9%
2.3437565033
 
7.7%
-0.127803996108661
12.9%
-73.98647308188330
22.4%
-81.3790435844181
 
5.3%
-87.6281280592281
11.0%
-115.1728745229512
27.3%

srch_dest_latitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.4459316
Minimum28.54129028
Maximum51.50753784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:42.125767image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum28.54129028
5-th percentile28.54129028
Q136.11466599
median40.75667954
Q341.88077927
95-th percentile51.50753784
Maximum51.50753784
Range22.96624756
Interquartile range (IQR)5.766113281

Descriptive statistics

Standard deviation6.056536844
Coefficient of variation (CV)0.1497440312
Kurtosis-0.3688261375
Mean40.4459316
Median Absolute Deviation (MAD)4.64201355
Skewness0.4398438668
Sum34019679.76
Variance36.68163854
MonotonicityNot monotonic
2021-07-06T23:58:42.186906image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
36.11466599229512
27.3%
40.75667954188330
22.4%
51.50753784108661
12.9%
41.8807792792281
11.0%
48.8627204965033
 
7.7%
28.5412902844181
 
5.3%
35.6749992439407
 
4.7%
34.7018966731007
 
3.7%
37.5659942626789
 
3.2%
41.8933296215914
 
1.9%
ValueCountFrequency (%)
28.5412902844181
 
5.3%
34.7018966731007
 
3.7%
35.6749992439407
 
4.7%
36.11466599229512
27.3%
37.5659942626789
 
3.2%
40.75667954188330
22.4%
41.8807792792281
11.0%
41.8933296215914
 
1.9%
48.8627204965033
 
7.7%
51.50753784108661
12.9%
ValueCountFrequency (%)
51.50753784108661
12.9%
48.8627204965033
 
7.7%
41.8933296215914
 
1.9%
41.8807792792281
11.0%
40.75667954188330
22.4%
37.5659942626789
 
3.2%
36.11466599229512
27.3%
35.6749992439407
 
4.7%
34.7018966731007
 
3.7%
28.5412902844181
 
5.3%

srch_ci
Categorical

HIGH CARDINALITY

Distinct385
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.7 MiB
2014-09-26
 
23604
2014-09-27
 
20095
2014-09-25
 
17645
2014-09-12
 
17519
2014-10-03
 
17397
Other values (380)
744855 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters8411150
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2015-02-25
2nd row2015-02-25
3rd row2015-02-25
4th row2015-02-25
5th row2015-02-25

Common Values

ValueCountFrequency (%)
2014-09-2623604
 
2.8%
2014-09-2720095
 
2.4%
2014-09-2517645
 
2.1%
2014-09-1217519
 
2.1%
2014-10-0317397
 
2.1%
2014-09-1917047
 
2.0%
2014-09-1716773
 
2.0%
2014-09-1816121
 
1.9%
2014-09-1516090
 
1.9%
2014-09-1315145
 
1.8%
Other values (375)663679
78.9%

Length

2021-07-06T23:58:42.367108image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2014-09-2623604
 
2.8%
2014-09-2720095
 
2.4%
2014-09-2517645
 
2.1%
2014-09-1217519
 
2.1%
2014-10-0317397
 
2.1%
2014-09-1917047
 
2.0%
2014-09-1716773
 
2.0%
2014-09-1816121
 
1.9%
2014-09-1516090
 
1.9%
2014-09-1315145
 
1.8%
Other values (375)663679
78.9%

Most occurring characters

ValueCountFrequency (%)
01859900
22.1%
11712432
20.4%
-1682230
20.0%
21270744
15.1%
4875706
10.4%
9480273
 
5.7%
5141312
 
1.7%
3125449
 
1.5%
791265
 
1.1%
688574
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6728920
80.0%
Dash Punctuation1682230
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01859900
27.6%
11712432
25.4%
21270744
18.9%
4875706
13.0%
9480273
 
7.1%
5141312
 
2.1%
3125449
 
1.9%
791265
 
1.4%
688574
 
1.3%
883265
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
-1682230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8411150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01859900
22.1%
11712432
20.4%
-1682230
20.0%
21270744
15.1%
4875706
10.4%
9480273
 
5.7%
5141312
 
1.7%
3125449
 
1.5%
791265
 
1.1%
688574
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8411150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01859900
22.1%
11712432
20.4%
-1682230
20.0%
21270744
15.1%
4875706
10.4%
9480273
 
5.7%
5141312
 
1.7%
3125449
 
1.5%
791265
 
1.1%
688574
 
1.1%

srch_co
Categorical

HIGH CARDINALITY

Distinct376
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.7 MiB
2014-09-28
 
28497
2014-10-05
 
20994
2014-09-14
 
19867
2014-09-21
 
19524
2014-09-19
 
18294
Other values (371)
733939 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters8411150
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015-02-28
2nd row2015-02-28
3rd row2015-02-28
4th row2015-02-28
5th row2015-02-28

Common Values

ValueCountFrequency (%)
2014-09-2828497
 
3.4%
2014-10-0520994
 
2.5%
2014-09-1419867
 
2.4%
2014-09-2119524
 
2.3%
2014-09-1918294
 
2.2%
2014-09-2615917
 
1.9%
2014-09-1815393
 
1.8%
2014-09-2915303
 
1.8%
2014-09-2714002
 
1.7%
2014-10-0313708
 
1.6%
Other values (366)659616
78.4%

Length

2021-07-06T23:58:42.566611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2014-09-2828497
 
3.4%
2014-10-0520994
 
2.5%
2014-09-1419867
 
2.4%
2014-09-2119524
 
2.3%
2014-09-1918294
 
2.2%
2014-09-2615917
 
1.9%
2014-09-1815393
 
1.8%
2014-09-2915303
 
1.8%
2014-09-2714002
 
1.7%
2014-10-0313708
 
1.6%
Other values (366)659616
78.4%

Most occurring characters

ValueCountFrequency (%)
01851353
22.0%
11753743
20.9%
-1682230
20.0%
21265309
15.0%
4869049
10.3%
9461090
 
5.5%
5151801
 
1.8%
3118517
 
1.4%
888405
 
1.1%
785033
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6728920
80.0%
Dash Punctuation1682230
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01851353
27.5%
11753743
26.1%
21265309
18.8%
4869049
12.9%
9461090
 
6.9%
5151801
 
2.3%
3118517
 
1.8%
888405
 
1.3%
785033
 
1.3%
684620
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
-1682230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8411150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01851353
22.0%
11753743
20.9%
-1682230
20.0%
21265309
15.0%
4869049
10.3%
9461090
 
5.5%
5151801
 
1.8%
3118517
 
1.4%
888405
 
1.1%
785033
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8411150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01851353
22.0%
11753743
20.9%
-1682230
20.0%
21265309
15.0%
4869049
10.3%
9461090
 
5.5%
5151801
 
1.8%
3118517
 
1.4%
888405
 
1.1%
785033
 
1.0%

srch_ci_day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.272101912
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:42.642021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.003198942
Coefficient of variation (CV)0.468902424
Kurtosis-1.237579205
Mean4.272101912
Median Absolute Deviation (MAD)2
Skewness-0.2313280916
Sum3593329
Variance4.012806002
MonotonicityNot monotonic
2021-07-06T23:58:42.712616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6169500
20.2%
7129966
15.5%
5124112
14.8%
4108793
12.9%
1105381
12.5%
2104635
12.4%
398728
11.7%
ValueCountFrequency (%)
1105381
12.5%
2104635
12.4%
398728
11.7%
4108793
12.9%
5124112
14.8%
6169500
20.2%
7129966
15.5%
ValueCountFrequency (%)
7129966
15.5%
6169500
20.2%
5124112
14.8%
4108793
12.9%
398728
11.7%
2104635
12.4%
1105381
12.5%

srch_co_day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.689650048
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:42.776106image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.128168459
Coefficient of variation (CV)0.5767941217
Kurtosis-1.390812408
Mean3.689650048
Median Absolute Deviation (MAD)2
Skewness0.1445899203
Sum3103420
Variance4.529100988
MonotonicityNot monotonic
2021-07-06T23:58:42.837774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1195518
23.2%
6123436
14.7%
2120999
14.4%
7105796
12.6%
5102095
12.1%
398843
11.8%
494428
11.2%
ValueCountFrequency (%)
1195518
23.2%
2120999
14.4%
398843
11.8%
494428
11.2%
5102095
12.1%
6123436
14.7%
7105796
12.6%
ValueCountFrequency (%)
7105796
12.6%
6123436
14.7%
5102095
12.1%
494428
11.2%
398843
11.8%
2120999
14.4%
1195518
23.2%

srch_los
Real number (ℝ≥0)

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.677237952
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:42.911300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum28
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.192103099
Coefficient of variation (CV)0.8187927776
Kurtosis18.15820755
Mean2.677237952
Median Absolute Deviation (MAD)1
Skewness3.176307195
Sum2251865
Variance4.805315997
MonotonicityNot monotonic
2021-07-06T23:58:42.987856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1279368
33.2%
2224572
26.7%
3152235
18.1%
478333
 
9.3%
540353
 
4.8%
620690
 
2.5%
717979
 
2.1%
86723
 
0.8%
105526
 
0.7%
94845
 
0.6%
Other values (17)10491
 
1.2%
ValueCountFrequency (%)
1279368
33.2%
2224572
26.7%
3152235
18.1%
478333
 
9.3%
540353
 
4.8%
620690
 
2.5%
717979
 
2.1%
86723
 
0.8%
94845
 
0.6%
105526
 
0.7%
ValueCountFrequency (%)
28363
< 0.1%
275
 
< 0.1%
258
 
< 0.1%
2434
 
< 0.1%
2394
 
< 0.1%
22134
 
< 0.1%
2143
 
< 0.1%
20337
< 0.1%
19190
< 0.1%
1895
 
< 0.1%

srch_bw
Real number (ℝ≥0)

ZEROS

Distinct370
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.34915677
Minimum0
Maximum473
Zeros68898
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:43.070022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median17
Q346
95-th percentile123
Maximum473
Range473
Interquartile range (IQR)42

Descriptive statistics

Standard deviation49.50678471
Coefficient of variation (CV)1.400508222
Kurtosis11.44619023
Mean35.34915677
Median Absolute Deviation (MAD)15
Skewness2.89395015
Sum29732706
Variance2450.921733
MonotonicityNot monotonic
2021-07-06T23:58:43.158387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
068898
 
8.2%
155718
 
6.6%
235314
 
4.2%
328591
 
3.4%
425924
 
3.1%
525637
 
3.0%
622257
 
2.6%
720416
 
2.4%
817128
 
2.0%
916471
 
2.0%
Other values (360)524761
62.4%
ValueCountFrequency (%)
068898
8.2%
155718
6.6%
235314
4.2%
328591
3.4%
425924
 
3.1%
525637
 
3.0%
622257
 
2.6%
720416
 
2.4%
817128
 
2.0%
916471
 
2.0%
ValueCountFrequency (%)
47350
< 0.1%
46810
 
< 0.1%
46410
 
< 0.1%
44310
 
< 0.1%
43342
< 0.1%
4219
 
< 0.1%
41850
< 0.1%
41610
 
< 0.1%
41550
< 0.1%
40948
< 0.1%

srch_adults_cnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.019900202
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:43.231454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum16
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.02062611
Coefficient of variation (CV)0.5052854142
Kurtosis25.4761709
Mean2.019900202
Median Absolute Deviation (MAD)0
Skewness3.580089139
Sum1698932
Variance1.041677657
MonotonicityNot monotonic
2021-07-06T23:58:43.294956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2533366
63.4%
1200294
 
23.8%
344628
 
5.3%
444508
 
5.3%
66854
 
0.8%
56046
 
0.7%
82165
 
0.3%
71085
 
0.1%
10680
 
0.1%
9559
 
0.1%
Other values (6)912
 
0.1%
ValueCountFrequency (%)
1200294
 
23.8%
2533366
63.4%
344628
 
5.3%
444508
 
5.3%
56046
 
0.7%
66854
 
0.8%
71085
 
0.1%
82165
 
0.3%
9559
 
0.1%
10680
 
0.1%
ValueCountFrequency (%)
16165
 
< 0.1%
1510
 
< 0.1%
14113
 
< 0.1%
13102
 
< 0.1%
12450
 
0.1%
1172
 
< 0.1%
10680
 
0.1%
9559
 
0.1%
82165
0.3%
71085
0.1%

srch_children_cnt
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.1614665134
Minimum0
Maximum8
Zeros760192
Zeros (%)90.4%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:43.363478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5460633829
Coefficient of variation (CV)3.381898646
Kurtosis17.04005121
Mean0.1614665134
Median Absolute Deviation (MAD)0
Skewness3.857360437
Sum135809
Variance0.2981852182
MonotonicityNot monotonic
2021-07-06T23:58:43.427474image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0760192
90.4%
136644
 
4.4%
235613
 
4.2%
37258
 
0.9%
41023
 
0.1%
5196
 
< 0.1%
6113
 
< 0.1%
749
 
< 0.1%
89
 
< 0.1%
(Missing)18
 
< 0.1%
ValueCountFrequency (%)
0760192
90.4%
136644
 
4.4%
235613
 
4.2%
37258
 
0.9%
41023
 
0.1%
5196
 
< 0.1%
6113
 
< 0.1%
749
 
< 0.1%
89
 
< 0.1%
ValueCountFrequency (%)
89
 
< 0.1%
749
 
< 0.1%
6113
 
< 0.1%
5196
 
< 0.1%
41023
 
0.1%
37258
 
0.9%
235613
 
4.2%
136644
 
4.4%
0760192
90.4%

srch_rm_cnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.113059451
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:43.493085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4476569378
Coefficient of variation (CV)0.4021860085
Kurtosis55.3619333
Mean1.113059451
Median Absolute Deviation (MAD)0
Skewness6.177560561
Sum936211
Variance0.2003967339
MonotonicityNot monotonic
2021-07-06T23:58:43.556621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1770809
91.6%
255399
 
6.6%
39300
 
1.1%
43256
 
0.4%
51332
 
0.2%
6439
 
0.1%
8326
 
< 0.1%
7254
 
< 0.1%
ValueCountFrequency (%)
1770809
91.6%
255399
 
6.6%
39300
 
1.1%
43256
 
0.4%
51332
 
0.2%
6439
 
0.1%
7254
 
< 0.1%
8326
 
< 0.1%
ValueCountFrequency (%)
8326
 
< 0.1%
7254
 
< 0.1%
6439
 
0.1%
51332
 
0.2%
43256
 
0.4%
39300
 
1.1%
255399
 
6.6%
1770809
91.6%

srch_mobile_bool
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.5 MiB
0
719008 
1
122107 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters841115
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0719008
85.5%
1122107
 
14.5%

Length

2021-07-06T23:58:43.701101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-06T23:58:43.747724image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0719008
85.5%
1122107
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0719008
85.5%
1122107
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number841115
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0719008
85.5%
1122107
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
Common841115
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0719008
85.5%
1122107
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII841115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0719008
85.5%
1122107
 
14.5%

srch_mobile_app
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.5 MiB
0
841115 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters841115
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0841115
100.0%

Length

2021-07-06T23:58:43.861860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-06T23:58:43.908155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0841115
100.0%

Most occurring characters

ValueCountFrequency (%)
0841115
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number841115
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0841115
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common841115
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0841115
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII841115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0841115
100.0%

srch_device
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.2 MiB
DESKTOP
718871 
TABWEB
106904 
WEB
 
15340

Length

Max length7
Median length7
Mean length6.799951255
Min length3

Characters and Unicode

Total characters5719541
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDESKTOP
2nd rowDESKTOP
3rd rowDESKTOP
4th rowDESKTOP
5th rowDESKTOP

Common Values

ValueCountFrequency (%)
DESKTOP718871
85.5%
TABWEB106904
 
12.7%
WEB15340
 
1.8%

Length

2021-07-06T23:58:44.036702image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-06T23:58:44.090367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
desktop718871
85.5%
tabweb106904
 
12.7%
web15340
 
1.8%

Most occurring characters

ValueCountFrequency (%)
E841115
14.7%
T825775
14.4%
D718871
12.6%
S718871
12.6%
K718871
12.6%
O718871
12.6%
P718871
12.6%
B229148
 
4.0%
W122244
 
2.1%
A106904
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5719541
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E841115
14.7%
T825775
14.4%
D718871
12.6%
S718871
12.6%
K718871
12.6%
O718871
12.6%
P718871
12.6%
B229148
 
4.0%
W122244
 
2.1%
A106904
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin5719541
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E841115
14.7%
T825775
14.4%
D718871
12.6%
S718871
12.6%
K718871
12.6%
O718871
12.6%
P718871
12.6%
B229148
 
4.0%
W122244
 
2.1%
A106904
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII5719541
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E841115
14.7%
T825775
14.4%
D718871
12.6%
S718871
12.6%
K718871
12.6%
O718871
12.6%
P718871
12.6%
B229148
 
4.0%
W122244
 
2.1%
A106904
 
1.9%

srch_currency
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct51
Distinct (%)< 0.1%
Missing134104
Missing (%)15.9%
Memory size44.5 MiB
USD
412926 
EUR
53654 
GBP
43749 
SEK
 
28876
NOK
 
20436
Other values (46)
147370 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2121033
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTWD
2nd rowTWD
3rd rowTWD
4th rowTWD
5th rowTWD

Common Values

ValueCountFrequency (%)
USD412926
49.1%
EUR53654
 
6.4%
GBP43749
 
5.2%
SEK28876
 
3.4%
NOK20436
 
2.4%
JPY18138
 
2.2%
CAD17762
 
2.1%
KRW17761
 
2.1%
HKD14999
 
1.8%
BRL13302
 
1.6%
Other values (41)65408
 
7.8%
(Missing)134104
 
15.9%

Length

2021-07-06T23:58:44.239767image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usd412926
58.4%
eur53654
 
7.6%
gbp43749
 
6.2%
sek28876
 
4.1%
nok20436
 
2.9%
jpy18138
 
2.6%
cad17762
 
2.5%
krw17761
 
2.5%
hkd14999
 
2.1%
brl13302
 
1.9%
Other values (41)65408
 
9.3%

Most occurring characters

ValueCountFrequency (%)
D480294
22.6%
U473947
22.3%
S449251
21.2%
K106235
 
5.0%
R94897
 
4.5%
E82929
 
3.9%
P65382
 
3.1%
B61799
 
2.9%
G48194
 
2.3%
N32778
 
1.5%
Other values (16)225327
10.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2121033
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D480294
22.6%
U473947
22.3%
S449251
21.2%
K106235
 
5.0%
R94897
 
4.5%
E82929
 
3.9%
P65382
 
3.1%
B61799
 
2.9%
G48194
 
2.3%
N32778
 
1.5%
Other values (16)225327
10.6%

Most occurring scripts

ValueCountFrequency (%)
Latin2121033
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D480294
22.6%
U473947
22.3%
S449251
21.2%
K106235
 
5.0%
R94897
 
4.5%
E82929
 
3.9%
P65382
 
3.1%
B61799
 
2.9%
G48194
 
2.3%
N32778
 
1.5%
Other values (16)225327
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2121033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D480294
22.6%
U473947
22.3%
S449251
21.2%
K106235
 
5.0%
R94897
 
4.5%
E82929
 
3.9%
P65382
 
3.1%
B61799
 
2.9%
G48194
 
2.3%
N32778
 
1.5%
Other values (16)225327
10.6%

prop_travelad_bool
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.5 MiB
0
830836 
1
 
10279

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters841115
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0830836
98.8%
110279
 
1.2%

Length

2021-07-06T23:58:44.372985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-06T23:58:44.419086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0830836
98.8%
110279
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0830836
98.8%
110279
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number841115
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0830836
98.8%
110279
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common841115
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0830836
98.8%
110279
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII841115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0830836
98.8%
110279
 
1.2%

prop_dotd_bool
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.5 MiB
0
837911 
1
 
3204

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters841115
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0837911
99.6%
13204
 
0.4%

Length

2021-07-06T23:58:44.529051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-06T23:58:44.573863image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0837911
99.6%
13204
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0837911
99.6%
13204
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number841115
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0837911
99.6%
13204
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common841115
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0837911
99.6%
13204
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII841115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0837911
99.6%
13204
 
0.4%

prop_price_without_discount_local
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct40511
Distinct (%)4.8%
Missing51
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12045.08004
Minimum8
Maximum31954891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:44.636878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile64
Q1169
median339
Q3665
95-th percentile17172
Maximum31954891
Range31954883
Interquartile range (IQR)496

Descriptive statistics

Standard deviation167927.2531
Coefficient of variation (CV)13.94156391
Kurtosis6315.146435
Mean12045.08004
Median Absolute Deviation (MAD)201
Skewness63.25387336
Sum1.01306832 × 1010
Variance2.819956234 × 1010
MonotonicityNot monotonic
2021-07-06T23:58:44.729446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49910167
 
1.2%
2998398
 
1.0%
1996631
 
0.8%
3996551
 
0.8%
5295969
 
0.7%
2795653
 
0.7%
1495436
 
0.6%
4595371
 
0.6%
3595334
 
0.6%
1294860
 
0.6%
Other values (40501)776694
92.3%
ValueCountFrequency (%)
84
 
< 0.1%
91
 
< 0.1%
1012
 
< 0.1%
1117
 
< 0.1%
1217
 
< 0.1%
1315
 
< 0.1%
1445
< 0.1%
1559
< 0.1%
1653
< 0.1%
1746
< 0.1%
ValueCountFrequency (%)
319548911
< 0.1%
296332731
< 0.1%
232490421
< 0.1%
192667511
< 0.1%
188111391
< 0.1%
180509921
< 0.1%
178928291
< 0.1%
175302721
< 0.1%
168310252
< 0.1%
162748681
< 0.1%

prop_price_without_discount_usd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct75478
Distinct (%)9.0%
Missing51
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean452.7639873
Minimum0
Maximum1976732.58
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:44.834270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile57
Q1144
median273
Q3449
95-th percentile749
Maximum1976732.58
Range1976732.58
Interquartile range (IQR)305

Descriptive statistics

Standard deviation6864.687807
Coefficient of variation (CV)15.16173547
Kurtosis18352.23406
Mean452.7639873
Median Absolute Deviation (MAD)144
Skewness110.1131851
Sum380803490.2
Variance47123938.69
MonotonicityNot monotonic
2021-07-06T23:58:44.933396image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4999881
 
1.2%
2997975
 
0.9%
3996262
 
0.7%
1996047
 
0.7%
5295847
 
0.7%
2795272
 
0.6%
4595192
 
0.6%
3595112
 
0.6%
1494935
 
0.6%
3294449
 
0.5%
Other values (75468)780092
92.7%
ValueCountFrequency (%)
02
 
< 0.1%
0.0163
< 0.1%
0.0269
< 0.1%
0.0358
< 0.1%
0.0446
< 0.1%
0.0545
< 0.1%
0.0635
< 0.1%
0.0740
< 0.1%
0.0830
< 0.1%
0.0923
 
< 0.1%
ValueCountFrequency (%)
1976732.581
< 0.1%
13477602
< 0.1%
12722051
< 0.1%
10871171
< 0.1%
10328951
< 0.1%
9525971
< 0.1%
8709721
< 0.1%
8165201
< 0.1%
7690731
< 0.1%
7399241
< 0.1%

prop_price_with_discount_local
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct37770
Distinct (%)4.5%
Missing51
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean8519.443857
Minimum6
Maximum17621297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:45.035943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile42
Q1116
median235
Q3466
95-th percentile12125
Maximum17621297
Range17621291
Interquartile range (IQR)350

Descriptive statistics

Standard deviation113014.2593
Coefficient of variation (CV)13.26545033
Kurtosis3729.459195
Mean8519.443857
Median Absolute Deviation (MAD)141
Skewness51.50835913
Sum7165397528
Variance1.27722228 × 1010
MonotonicityNot monotonic
2021-07-06T23:58:45.131360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1997129
 
0.8%
2996642
 
0.8%
696236
 
0.7%
1596023
 
0.7%
2295689
 
0.7%
1795598
 
0.7%
995271
 
0.6%
2795088
 
0.6%
895028
 
0.6%
1494940
 
0.6%
Other values (37760)783420
93.1%
ValueCountFrequency (%)
61
 
< 0.1%
73
 
< 0.1%
89
 
< 0.1%
931
 
< 0.1%
1037
 
< 0.1%
1176
 
< 0.1%
1297
 
< 0.1%
13111
 
< 0.1%
14449
0.1%
15337
< 0.1%
ValueCountFrequency (%)
176212971
< 0.1%
123054511
< 0.1%
118679281
< 0.1%
116947171
< 0.1%
114419111
< 0.1%
111014351
< 0.1%
108197741
< 0.1%
104621741
< 0.1%
102630941
< 0.1%
100993501
< 0.1%

prop_price_with_discount_usd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct59865
Distinct (%)7.1%
Missing51
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean304.4565213
Minimum0
Maximum1087117
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:45.236350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38.9815
Q199.02
median189
Q3299
95-th percentile499
Maximum1087117
Range1087117
Interquartile range (IQR)199.98

Descriptive statistics

Standard deviation4205.190231
Coefficient of variation (CV)13.81212074
Kurtosis12429.72469
Mean304.4565213
Median Absolute Deviation (MAD)97.57
Skewness92.09231784
Sum256067419.6
Variance17683624.88
MonotonicityNot monotonic
2021-07-06T23:58:45.337668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2996399
 
0.8%
1996378
 
0.8%
695731
 
0.7%
1595335
 
0.6%
2295318
 
0.6%
1794994
 
0.6%
2794821
 
0.6%
2694458
 
0.5%
2494415
 
0.5%
994350
 
0.5%
Other values (59855)788865
93.8%
ValueCountFrequency (%)
04
 
< 0.1%
0.0194
< 0.1%
0.0295
< 0.1%
0.0375
< 0.1%
0.0434
 
< 0.1%
0.0538
 
< 0.1%
0.0647
< 0.1%
0.0767
< 0.1%
0.0851
< 0.1%
0.0949
< 0.1%
ValueCountFrequency (%)
10871171
< 0.1%
7454521
< 0.1%
6569041
< 0.1%
6367401
< 0.1%
5856291
< 0.1%
5245201
< 0.1%
5238601
< 0.1%
4972541
< 0.1%
4855791
< 0.1%
4182951
< 0.1%

prop_imp_drr
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.5 MiB
0
482802 
1
358313 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters841115
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0482802
57.4%
1358313
42.6%

Length

2021-07-06T23:58:45.492689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-06T23:58:45.539142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0482802
57.4%
1358313
42.6%

Most occurring characters

ValueCountFrequency (%)
0482802
57.4%
1358313
42.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number841115
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0482802
57.4%
1358313
42.6%

Most occurring scripts

ValueCountFrequency (%)
Common841115
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0482802
57.4%
1358313
42.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII841115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0482802
57.4%
1358313
42.6%

prop_booking_bool
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.5 MiB
0
817102 
1
 
24013

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters841115
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0817102
97.1%
124013
 
2.9%

Length

2021-07-06T23:58:45.659968image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-06T23:58:45.709116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0817102
97.1%
124013
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0817102
97.1%
124013
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number841115
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0817102
97.1%
124013
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common841115
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0817102
97.1%
124013
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII841115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0817102
97.1%
124013
 
2.9%

prop_brand_bool
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.5 MiB
1
558600 
0
282515 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters841115
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1558600
66.4%
0282515
33.6%

Length

2021-07-06T23:58:45.825133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-06T23:58:45.871685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1558600
66.4%
0282515
33.6%

Most occurring characters

ValueCountFrequency (%)
1558600
66.4%
0282515
33.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number841115
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1558600
66.4%
0282515
33.6%

Most occurring scripts

ValueCountFrequency (%)
Common841115
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1558600
66.4%
0282515
33.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII841115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1558600
66.4%
0282515
33.6%

prop_starrating
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.595627233
Minimum0
Maximum5
Zeros10593
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:45.912353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8645742241
Coefficient of variation (CV)0.240451573
Kurtosis2.460363408
Mean3.595627233
Median Absolute Deviation (MAD)0.5
Skewness-0.9343957152
Sum3024336
Variance0.7474885889
MonotonicityNot monotonic
2021-07-06T23:58:45.975938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4300791
35.8%
3210900
25.1%
3.5104581
 
12.4%
580056
 
9.5%
4.555581
 
6.6%
237710
 
4.5%
2.535516
 
4.2%
010593
 
1.3%
1.54894
 
0.6%
1493
 
0.1%
ValueCountFrequency (%)
010593
 
1.3%
1493
 
0.1%
1.54894
 
0.6%
237710
 
4.5%
2.535516
 
4.2%
3210900
25.1%
3.5104581
 
12.4%
4300791
35.8%
4.555581
 
6.6%
580056
 
9.5%
ValueCountFrequency (%)
580056
 
9.5%
4.555581
 
6.6%
4300791
35.8%
3.5104581
 
12.4%
3210900
25.1%
2.535516
 
4.2%
237710
 
4.5%
1.54894
 
0.6%
1493
 
0.1%
010593
 
1.3%

prop_super_region
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.9 MiB
AMER
554304 
EMEA
189606 
APAC
97203 
LATAM
 
2

Length

Max length5
Median length4
Mean length4.000002378
Min length4

Characters and Unicode

Total characters3364462
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAPAC
2nd rowAPAC
3rd rowAPAC
4th rowAPAC
5th rowAPAC

Common Values

ValueCountFrequency (%)
AMER554304
65.9%
EMEA189606
 
22.5%
APAC97203
 
11.6%
LATAM2
 
< 0.1%

Length

2021-07-06T23:58:46.119901image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-06T23:58:46.170716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
amer554304
65.9%
emea189606
 
22.5%
apac97203
 
11.6%
latam2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A938320
27.9%
E933516
27.7%
M743912
22.1%
R554304
16.5%
P97203
 
2.9%
C97203
 
2.9%
L2
 
< 0.1%
T2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3364462
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A938320
27.9%
E933516
27.7%
M743912
22.1%
R554304
16.5%
P97203
 
2.9%
C97203
 
2.9%
L2
 
< 0.1%
T2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin3364462
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A938320
27.9%
E933516
27.7%
M743912
22.1%
R554304
16.5%
P97203
 
2.9%
C97203
 
2.9%
L2
 
< 0.1%
T2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3364462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A938320
27.9%
E933516
27.7%
M743912
22.1%
R554304
16.5%
P97203
 
2.9%
C97203
 
2.9%
L2
 
< 0.1%
T2
 
< 0.1%

prop_continent
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 MiB
NORTHAMERICA
554304 
EUROPE
189606 
ASIA
97203 
LATAM
 
2

Length

Max length12
Median length12
Mean length9.722934438
Min length4

Characters and Unicode

Total characters8178106
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowASIA
2nd rowASIA
3rd rowASIA
4th rowASIA
5th rowASIA

Common Values

ValueCountFrequency (%)
NORTHAMERICA554304
65.9%
EUROPE189606
 
22.5%
ASIA97203
 
11.6%
LATAM2
 
< 0.1%

Length

2021-07-06T23:58:46.299092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-06T23:58:46.350782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
northamerica554304
65.9%
europe189606
 
22.5%
asia97203
 
11.6%
latam2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A1303018
15.9%
R1298214
15.9%
E933516
11.4%
O743910
9.1%
I651507
8.0%
T554306
6.8%
M554306
6.8%
N554304
6.8%
H554304
6.8%
C554304
6.8%
Other values (4)476417
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter8178106
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1303018
15.9%
R1298214
15.9%
E933516
11.4%
O743910
9.1%
I651507
8.0%
T554306
6.8%
M554306
6.8%
N554304
6.8%
H554304
6.8%
C554304
6.8%
Other values (4)476417
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Latin8178106
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1303018
15.9%
R1298214
15.9%
E933516
11.4%
O743910
9.1%
I651507
8.0%
T554306
6.8%
M554306
6.8%
N554304
6.8%
H554304
6.8%
C554304
6.8%
Other values (4)476417
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII8178106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A1303018
15.9%
R1298214
15.9%
E933516
11.4%
O743910
9.1%
I651507
8.0%
T554306
6.8%
M554306
6.8%
N554304
6.8%
H554304
6.8%
C554304
6.8%
Other values (4)476417
 
5.8%

prop_country
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.9 MiB
UNITED STATES OF AMERICA
554304 
UNITED KINGDOM
108661 
JAPAN
70414 
FRANCE
65031 
SOUTH KOREA
 
26789
Other values (2)
 
15916

Length

Max length24
Median length24
Mean length18.9523133
Min length5

Characters and Unicode

Total characters15941075
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJAPAN
2nd rowJAPAN
3rd rowJAPAN
4th rowJAPAN
5th rowJAPAN

Common Values

ValueCountFrequency (%)
UNITED STATES OF AMERICA554304
65.9%
UNITED KINGDOM108661
 
12.9%
JAPAN70414
 
8.4%
FRANCE65031
 
7.7%
SOUTH KOREA26789
 
3.2%
ITALY15914
 
1.9%
MARTINIQUE2
 
< 0.1%

Length

2021-07-06T23:58:46.477515image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-06T23:58:46.532170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
united662965
25.1%
states554304
21.0%
of554304
21.0%
america554304
21.0%
kingdom108661
 
4.1%
japan70414
 
2.7%
france65031
 
2.5%
south26789
 
1.0%
korea26789
 
1.0%
italy15914
 
0.6%

Most occurring characters

ValueCountFrequency (%)
A1911476
12.0%
E1863395
11.7%
T1814278
11.4%
1798362
11.3%
I1341848
8.4%
S1135397
 
7.1%
N907073
 
5.7%
D771626
 
4.8%
O716543
 
4.5%
U689756
 
4.3%
Other values (12)2991321
18.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter14142713
88.7%
Space Separator1798362
 
11.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1911476
13.5%
E1863395
13.2%
T1814278
12.8%
I1341848
9.5%
S1135397
8.0%
N907073
 
6.4%
D771626
 
5.5%
O716543
 
5.1%
U689756
 
4.9%
M662967
 
4.7%
Other values (11)2328354
16.5%
Space Separator
ValueCountFrequency (%)
1798362
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14142713
88.7%
Common1798362
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1911476
13.5%
E1863395
13.2%
T1814278
12.8%
I1341848
9.5%
S1135397
8.0%
N907073
 
6.4%
D771626
 
5.5%
O716543
 
5.1%
U689756
 
4.9%
M662967
 
4.7%
Other values (11)2328354
16.5%
Common
ValueCountFrequency (%)
1798362
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15941075
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A1911476
12.0%
E1863395
11.7%
T1814278
11.4%
1798362
11.3%
I1341848
8.4%
S1135397
 
7.1%
N907073
 
5.7%
D771626
 
4.8%
O716543
 
4.5%
U689756
 
4.3%
Other values (12)2991321
18.8%

prop_market_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71586.9801
Minimum369
Maximum116356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:46.613432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum369
5-th percentile369
Q160039
median95602
Q395656
95-th percentile95664
Maximum116356
Range115987
Interquartile range (IQR)35617

Descriptive statistics

Standard deviation39165.98576
Coefficient of variation (CV)0.547110462
Kurtosis-0.4814647598
Mean71586.9801
Median Absolute Deviation (MAD)54
Skewness-1.169138482
Sum6.021288277 × 1010
Variance1533974440
MonotonicityNot monotonic
2021-07-06T23:58:46.703281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95602229390
27.3%
95656184846
22.0%
369108616
12.9%
9561290745
 
10.8%
40762111
 
7.4%
9566444193
 
5.3%
6003935406
 
4.2%
6004129015
 
3.4%
9098826760
 
3.2%
42615360
 
1.8%
Other values (48)14673
 
1.7%
ValueCountFrequency (%)
369108616
12.9%
40762111
7.4%
42615360
 
1.8%
60037527
 
0.1%
6003935406
 
4.2%
6004129015
 
3.4%
60475113
 
< 0.1%
61469115
 
< 0.1%
615143548
 
0.4%
9098826760
 
3.2%
ValueCountFrequency (%)
1163562423
0.3%
1163162
 
< 0.1%
1083471
 
< 0.1%
108227103
 
< 0.1%
102274174
 
< 0.1%
102132554
 
0.1%
1020591
 
< 0.1%
10204620
 
< 0.1%
1020451
 
< 0.1%
10200752
 
< 0.1%

prop_submarket_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct294
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106494.2707
Minimum60556
Maximum116928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:47.586763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum60556
5-th percentile91279
Q198238
median109153
Q3110287
95-th percentile114514
Maximum116928
Range56372
Interquartile range (IQR)12049

Descriptive statistics

Standard deviation7384.185494
Coefficient of variation (CV)0.06933880526
Kurtosis2.792948565
Mean106494.2707
Median Absolute Deviation (MAD)1134
Skewness-1.39997588
Sum8.957392846 × 1010
Variance54526195.41
MonotonicityNot monotonic
2021-07-06T23:58:47.675904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110287113100
 
13.4%
9681281095
 
9.6%
11002951658
 
6.1%
10867049141
 
5.8%
9680938493
 
4.6%
10857731658
 
3.8%
10915325332
 
3.0%
10909019964
 
2.4%
10804617407
 
2.1%
11364417379
 
2.1%
Other values (284)395888
47.1%
ValueCountFrequency (%)
6055624
 
< 0.1%
605901917
 
0.2%
841992
 
< 0.1%
858673772
0.4%
858685285
0.6%
86632609
 
0.1%
89450117
 
< 0.1%
90991254
 
< 0.1%
909923462
0.4%
90995804
 
0.1%
ValueCountFrequency (%)
1169281135
0.1%
11690580
 
< 0.1%
116840622
0.1%
1168229
 
< 0.1%
1168147
 
< 0.1%
116786985
0.1%
11676810
 
< 0.1%
11673719
 
< 0.1%
11672499
 
< 0.1%
11672329
 
< 0.1%

prop_room_capacity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct698
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean631.1164573
Minimum-9998
Maximum19235
Zeros1770
Zeros (%)0.2%
Negative10401
Negative (%)1.2%
Memory size6.4 MiB
2021-07-06T23:58:47.769267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-9998
5-th percentile31
Q1144
median306
Q3770
95-th percentile3066
Maximum19235
Range29233
Interquartile range (IQR)626

Descriptive statistics

Standard deviation1573.071335
Coefficient of variation (CV)2.492521494
Kurtosis24.15277449
Mean631.1164573
Median Absolute Deviation (MAD)218
Skewness-3.144379732
Sum530841519
Variance2474553.424
MonotonicityNot monotonic
2021-07-06T23:58:47.854874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20012256
 
1.5%
-999810401
 
1.2%
15006435
 
0.8%
7115956
 
0.7%
1355129
 
0.6%
37704862
 
0.6%
40494641
 
0.6%
25004615
 
0.5%
40084580
 
0.5%
3164570
 
0.5%
Other values (688)777670
92.5%
ValueCountFrequency (%)
-999810401
1.2%
01770
 
0.2%
1282
 
< 0.1%
270
 
< 0.1%
3160
 
< 0.1%
4332
 
< 0.1%
5629
 
0.1%
6450
 
0.1%
7263
 
< 0.1%
8304
 
< 0.1%
ValueCountFrequency (%)
192351
 
< 0.1%
50054068
0.5%
42043821
0.5%
40494641
0.6%
40084580
0.5%
40044347
0.5%
39334465
0.5%
37704862
0.6%
37002947
0.4%
3680316
 
< 0.1%

prop_review_score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct4037
Distinct (%)0.5%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.024047683
Minimum0
Maximum5
Zeros5809
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:47.942211image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.2
Q13.8
median4.1
Q34.4
95-th percentile4.6
Maximum5
Range5
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.5471696756
Coefficient of variation (CV)0.1359749483
Kurtosis18.77027841
Mean4.024047683
Median Absolute Deviation (MAD)0.3
Skewness-3.086796013
Sum3384654.674
Variance0.2993946539
MonotonicityNot monotonic
2021-07-06T23:58:48.034018image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.477198
 
9.2%
4.374259
 
8.8%
4.573048
 
8.7%
472798
 
8.7%
4.272747
 
8.6%
4.167897
 
8.1%
3.953062
 
6.3%
3.843638
 
5.2%
3.742907
 
5.1%
3.639779
 
4.7%
Other values (4027)223774
26.6%
ValueCountFrequency (%)
05809
0.7%
0.77272727271
 
< 0.1%
1104
 
< 0.1%
1.31
 
< 0.1%
1.42
 
< 0.1%
1.436
 
< 0.1%
1.41
 
< 0.1%
1.4233891691
 
< 0.1%
1.5100
 
< 0.1%
1.61
 
< 0.1%
ValueCountFrequency (%)
5799
0.1%
4.922520
 
< 0.1%
4.91642
 
< 0.1%
4.92
 
< 0.1%
4.96
 
< 0.1%
4.96
 
< 0.1%
4.94
 
< 0.1%
4.94
 
< 0.1%
4.99
 
< 0.1%
4.93
 
< 0.1%

prop_review_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4626
Distinct (%)0.5%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2160.753578
Minimum0
Maximum32399
Zeros5831
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-07-06T23:58:48.123864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39
Q1306
median937
Q32550
95-th percentile8115
Maximum32399
Range32399
Interquartile range (IQR)2244

Descriptive statistics

Standard deviation3075.169206
Coefficient of variation (CV)1.423192925
Kurtosis7.156989011
Mean2160.753578
Median Absolute Deviation (MAD)748
Skewness2.45832707
Sum1817424960
Variance9456665.647
MonotonicityNot monotonic
2021-07-06T23:58:48.213732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05831
 
0.7%
362065
 
0.2%
21947
 
0.2%
11791
 
0.2%
371589
 
0.2%
71499
 
0.2%
11541458
 
0.2%
521359
 
0.2%
341301
 
0.2%
2001288
 
0.2%
Other values (4616)820979
97.6%
ValueCountFrequency (%)
05831
0.7%
11791
 
0.2%
21947
 
0.2%
31283
 
0.2%
41040
 
0.1%
5899
 
0.1%
61031
 
0.1%
71499
 
0.2%
8823
 
0.1%
9695
 
0.1%
ValueCountFrequency (%)
3239972
< 0.1%
2917623
 
< 0.1%
24518157
< 0.1%
2371592
< 0.1%
2211160
 
< 0.1%
203547
 
< 0.1%
1835335
 
< 0.1%
1769188
< 0.1%
171971
 
< 0.1%
1629262
 
< 0.1%

prop_hostel_bool
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.5 MiB
0
838686 
1
 
2429

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters841115
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0838686
99.7%
12429
 
0.3%

Length

2021-07-06T23:58:48.360003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-06T23:58:48.407347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0838686
99.7%
12429
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0838686
99.7%
12429
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number841115
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0838686
99.7%
12429
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common841115
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0838686
99.7%
12429
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII841115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0838686
99.7%
12429
 
0.3%

srch_local_date
Categorical

HIGH CORRELATION

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.7 MiB
2014-09-03
 
39487
2014-09-09
 
38261
2014-09-08
 
37843
2014-09-02
 
37839
2014-09-04
 
37662
Other values (23)
650023 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters8411150
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014-09-13
2nd row2014-09-13
3rd row2014-09-13
4th row2014-09-13
5th row2014-09-13

Common Values

ValueCountFrequency (%)
2014-09-0339487
 
4.7%
2014-09-0938261
 
4.5%
2014-09-0837843
 
4.5%
2014-09-0237839
 
4.5%
2014-09-0437662
 
4.5%
2014-09-1737515
 
4.5%
2014-09-1036488
 
4.3%
2014-09-1535691
 
4.2%
2014-09-1135239
 
4.2%
2014-09-1234176
 
4.1%
Other values (18)470914
56.0%

Length

2021-07-06T23:58:48.547800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2014-09-0339487
 
4.7%
2014-09-0938261
 
4.5%
2014-09-0837843
 
4.5%
2014-09-0237839
 
4.5%
2014-09-0437662
 
4.5%
2014-09-1737515
 
4.5%
2014-09-1036488
 
4.3%
2014-09-1535691
 
4.2%
2014-09-1135239
 
4.2%
2014-09-1234176
 
4.1%
Other values (18)470914
56.0%

Most occurring characters

ValueCountFrequency (%)
02031576
24.2%
-1682230
20.0%
11255419
14.9%
21151305
13.7%
4936046
11.1%
9862430
10.3%
3120842
 
1.4%
597575
 
1.2%
894122
 
1.1%
790693
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6728920
80.0%
Dash Punctuation1682230
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02031576
30.2%
11255419
18.7%
21151305
17.1%
4936046
13.9%
9862430
12.8%
3120842
 
1.8%
597575
 
1.5%
894122
 
1.4%
790693
 
1.3%
688912
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
-1682230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8411150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02031576
24.2%
-1682230
20.0%
11255419
14.9%
21151305
13.7%
4936046
11.1%
9862430
10.3%
3120842
 
1.4%
597575
 
1.2%
894122
 
1.1%
790693
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8411150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02031576
24.2%
-1682230
20.0%
11255419
14.9%
21151305
13.7%
4936046
11.1%
9862430
10.3%
3120842
 
1.4%
597575
 
1.2%
894122
 
1.1%
790693
 
1.1%

Interactions

2021-07-06T23:56:53.038798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:53.349854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:53.521327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:53.695110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:53.862902image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:54.029455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:54.185158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:54.346152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:54.509527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:54.670845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:54.834471image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:55.002660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:55.171459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:55.340307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:55.514205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:55.695611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:55.867968image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:56.050298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:56.213928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:56.380511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:56.552956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:56.745640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:56.924133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:57.082225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:57.254040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:57.420985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:57.586600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:57.759486image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:57.939780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:56:58.105051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-07-06T23:58:06.636442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:06.807486image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:06.979280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:07.158785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:07.317289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:07.485529image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:07.651910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:07.834625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:08.008521image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:08.169974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:08.333878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:08.495659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:08.652749image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:08.816961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:08.989514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:09.143771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:09.315413image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:09.475741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:09.633763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:09.798061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:09.964438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:10.130939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:10.299148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:10.471221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:10.645422image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:10.813260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:10.990721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:11.145696image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:11.314066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:11.482323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:11.662987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:11.836566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:11.998594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:12.163192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:12.324743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:12.482899image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:12.647759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:12.817700image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:12.978573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:13.136878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:13.300767image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:13.459508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:13.624366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:13.790450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:13.964560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:14.135855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:14.303810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:14.474658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:14.643468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:14.823100image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:14.979798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:15.144329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:15.307583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:15.488378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:15.663561image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:15.837452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:16.007326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:16.172776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:16.340723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:16.499072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:16.671448image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:16.835790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:16.999856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:17.166118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:17.328980image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:17.483062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:17.639973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:17.804877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:17.961039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:18.119596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:18.283871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:18.443138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:18.613142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:18.776042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:18.947347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:19.122506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:19.298565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:19.464050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:19.632150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:19.804809image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:19.975844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:20.141229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:20.303459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:20.464393image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:20.636038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:20.802889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:20.978500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:21.158141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:21.313978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:21.472357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:21.638576image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:22.132183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:22.294725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:22.459437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:22.620328image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:22.791353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:22.955420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:23.127274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:23.330188image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:23.532687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-06T23:58:23.772385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-07-06T23:58:48.757583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-06T23:58:49.006581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-06T23:58:49.260522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-06T23:58:49.521296image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-06T23:58:49.774383image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-06T23:58:25.821460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-06T23:58:29.022863image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-07-06T23:58:35.924075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-07-06T23:58:37.746122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

srch_idprop_keysrch_date_timesrch_visitor_idsrch_visitor_visit_nbrsrch_visitor_loc_countrysrch_visitor_loc_regionsrch_visitor_loc_citysrch_visitor_wr_membersrch_posa_continentsrch_posa_countrysrch_hcom_destination_idsrch_dest_longitudesrch_dest_latitudesrch_cisrch_cosrch_ci_daysrch_co_daysrch_lossrch_bwsrch_adults_cntsrch_children_cntsrch_rm_cntsrch_mobile_boolsrch_mobile_appsrch_devicesrch_currencyprop_travelad_boolprop_dotd_boolprop_price_without_discount_localprop_price_without_discount_usdprop_price_with_discount_localprop_price_with_discount_usdprop_imp_drrprop_booking_boolprop_brand_boolprop_starratingprop_super_regionprop_continentprop_countryprop_market_idprop_submarket_idprop_room_capacityprop_review_scoreprop_review_countprop_hostel_boolsrch_local_date
0-10463227132576902014-09-13 18:37:329373b009-4e10-495a-afae-204dd1fe4b7c5TWNTPETAIPEISigned in - Persistent|WR Member|Remembered FC MemberASIATAIWAN, REPUBLIC OF CHINA728660135.49815434.7018972015-02-252015-02-284731642.00.0100DESKTOPTWD005605.0186.723356.0111.800013.5APACASIAJAPAN600411091405754.1403.002014-09-13
1-104632271330662182014-09-13 18:37:329373b009-4e10-495a-afae-204dd1fe4b7c5TWNTPETAIPEISigned in - Persistent|WR Member|Remembered FC MemberASIATAIWAN, REPUBLIC OF CHINA728660135.49815434.7018972015-02-252015-02-284731642.00.0100DESKTOPTWD004614.0153.712769.092.241003.0APACASIAJAPAN600411091403393.6101.002014-09-13
2-104632271322719872014-09-13 18:37:329373b009-4e10-495a-afae-204dd1fe4b7c5TWNTPETAIPEISigned in - Persistent|WR Member|Remembered FC MemberASIATAIWAN, REPUBLIC OF CHINA728660135.49815434.7018972015-02-252015-02-284731642.00.0100DESKTOPTWD0014026.0467.252821.093.980013.5APACASIAJAPAN600411091401794.11189.002014-09-13
3-104632271333080252014-09-13 18:37:329373b009-4e10-495a-afae-204dd1fe4b7c5TWNTPETAIPEISigned in - Persistent|WR Member|Remembered FC MemberASIATAIWAN, REPUBLIC OF CHINA728660135.49815434.7018972015-02-252015-02-284731642.00.0100DESKTOPTWD0014308.0476.655202.0173.300015.0APACASIAJAPAN60041982782724.8221.002014-09-13
4-104632271332220462014-09-13 18:37:329373b009-4e10-495a-afae-204dd1fe4b7c5TWNTPETAIPEISigned in - Persistent|WR Member|Remembered FC MemberASIATAIWAN, REPUBLIC OF CHINA728660135.49815434.7018972015-02-252015-02-284731642.00.0100DESKTOPTWD005445.0181.392589.086.250003.0APACASIAJAPAN600411091401983.9702.002014-09-13
5-10463227132538082014-09-13 18:37:329373b009-4e10-495a-afae-204dd1fe4b7c5TWNTPETAIPEISigned in - Persistent|WR Member|Remembered FC MemberASIATAIWAN, REPUBLIC OF CHINA728660135.49815434.7018972015-02-252015-02-284731642.00.0100DESKTOPTWD006292.0209.615348.0178.161014.0APACASIAJAPAN600411091406434.3694.002014-09-13
6-104632271339068212014-09-13 18:37:329373b009-4e10-495a-afae-204dd1fe4b7c5TWNTPETAIPEISigned in - Persistent|WR Member|Remembered FC MemberASIATAIWAN, REPUBLIC OF CHINA728660135.49815434.7018972015-02-252015-02-284731642.00.0100DESKTOPTWD003132.0104.343132.0104.341003.0APACASIAJAPAN600411091401044.710.002014-09-13
7-10463227135427652014-09-13 18:37:329373b009-4e10-495a-afae-204dd1fe4b7c5TWNTPETAIPEISigned in - Persistent|WR Member|Remembered FC MemberASIATAIWAN, REPUBLIC OF CHINA728660135.49815434.7018972015-02-252015-02-284731642.00.0100DESKTOPTWD003523.0117.362672.089.010014.0APACASIAJAPAN600411091401344.0284.002014-09-13
8-10463227135229602014-09-13 18:37:329373b009-4e10-495a-afae-204dd1fe4b7c5TWNTPETAIPEISigned in - Persistent|WR Member|Remembered FC MemberASIATAIWAN, REPUBLIC OF CHINA728660135.49815434.7018972015-02-252015-02-284731642.00.0100DESKTOPTWD005686.0189.423295.0109.770013.5APACASIAJAPAN600411091403484.31800.002014-09-13
9-10463227133498532014-09-13 18:37:329373b009-4e10-495a-afae-204dd1fe4b7c5TWNTPETAIPEISigned in - Persistent|WR Member|Remembered FC MemberASIATAIWAN, REPUBLIC OF CHINA728660135.49815434.7018972015-02-252015-02-284731642.00.0100DESKTOPTWD001994.066.431934.064.431003.0APACASIAJAPAN600411091403023.8475.002014-09-13

Last rows

srch_idprop_keysrch_date_timesrch_visitor_idsrch_visitor_visit_nbrsrch_visitor_loc_countrysrch_visitor_loc_regionsrch_visitor_loc_citysrch_visitor_wr_membersrch_posa_continentsrch_posa_countrysrch_hcom_destination_idsrch_dest_longitudesrch_dest_latitudesrch_cisrch_cosrch_ci_daysrch_co_daysrch_lossrch_bwsrch_adults_cntsrch_children_cntsrch_rm_cntsrch_mobile_boolsrch_mobile_appsrch_devicesrch_currencyprop_travelad_boolprop_dotd_boolprop_price_without_discount_localprop_price_without_discount_usdprop_price_with_discount_localprop_price_with_discount_usdprop_imp_drrprop_booking_boolprop_brand_boolprop_starratingprop_super_regionprop_continentprop_countryprop_market_idprop_submarket_idprop_room_capacityprop_review_scoreprop_review_countprop_hostel_boolsrch_local_date
8411059647834092434252014-09-06 14:16:4740db5c41-4658-439b-ab81-ed6fed7fc8b95UNITED STATES OF AMERICAORPORTLANDNaNNaNUS1506246-73.98647340.756682014-09-072014-09-0913212.00.0100DESKTOPNaN00519.0519.0339.0339.00004.0AMERNORTHAMERICAUNITED STATES OF AMERICA95656968104233.71827.002014-09-06
8411069647834092582752014-09-06 14:16:4740db5c41-4658-439b-ab81-ed6fed7fc8b95UNITED STATES OF AMERICAORPORTLANDNaNNaNUS1506246-73.98647340.756682014-09-072014-09-0913212.00.0100DESKTOPNaN00619.0619.0439.0439.00014.0AMERNORTHAMERICAUNITED STATES OF AMERICA956569680913114.23505.002014-09-06
8411079647834093241552014-09-06 14:16:4740db5c41-4658-439b-ab81-ed6fed7fc8b95UNITED STATES OF AMERICAORPORTLANDNaNNaNUS1506246-73.98647340.756682014-09-072014-09-0913212.00.0100DESKTOPNaN00569.0569.0254.0254.00013.0AMERNORTHAMERICAUNITED STATES OF AMERICA95656968081584.1442.002014-09-06
8411089647834094954242014-09-06 14:16:4740db5c41-4658-439b-ab81-ed6fed7fc8b95UNITED STATES OF AMERICAORPORTLANDNaNNaNUS1506246-73.98647340.756682014-09-072014-09-0913212.00.0100DESKTOPNaN00539.0539.0299.0299.00014.0AMERNORTHAMERICAUNITED STATES OF AMERICA956561086122224.4761.002014-09-06
8411099647834092851852014-09-06 14:16:4740db5c41-4658-439b-ab81-ed6fed7fc8b95UNITED STATES OF AMERICAORPORTLANDNaNNaNUS1506246-73.98647340.756682014-09-072014-09-0913212.00.0100DESKTOPNaN00419.0419.0388.0388.01014.0AMERNORTHAMERICAUNITED STATES OF AMERICA95656968128043.62745.002014-09-06
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